1. Field of Application
The present invention relates to a printing signal correction and printer operation control apparatus for use in applications such as an image copier apparatus of a type which is capable of printing an image having at least intermediate gray-scale gradations.
2. Prior Art Technology
FIG. 1 is a simple block diagram showing the basic arrangement of an image copier apparatus which executes image scanning/analysis, and printing functions. For the purpose of description, it will be assumed that the apparatus of FIG. 1 is a color copier apparatus, however the basic principles are equally applicable to a monochrome copier apparatus or to a monochrome or color facsimile apparatus. It will also be assumed that the apparatus executes correction of color image analysis signals (obtained by scanning an original color image) to obtain color printing density signals for use in printing a copy of that original image, in accordance with the aforementioned U.S. application of which the present invention is a continuation in part, i.e. that the apparatus executes such correction by means of processing the image analysis signals in a neural network. A scanning/analysis section 11 serves to scan successive small regions (i.e. picture elements) of a source image 10, to obtain for each picture element a tricolor R,G, B (red, green, blue) set of primary color data values, with the flow of color data values being designated as an output scanning signal A from the scanning/analysis section 10. These data values are supplied to a neural network section 12, which executes correction of each color data value to obtain a corresponding set of printing color data values, i.e. a set of R, G, B or C, M, Y (cyan, magenta, yellow), or C, M, Y, B data values, where B is a black level component, with the flow of printing color data values being designated as an output printing signal B from the neural network section 12. The printing color data values are supplied to a printer unit 13, and the correction that is applied to each set of analysis tricolor data values in the neural network section 12 should ideally be such as to obtain a corresponding set of printing data values which will result in optimum matching between each original picture element of the source image 10 and a corresponding printed picture element of an output copy image '14 that is generated by the printer unit 13 in response to the color printing data values. That is to say, the neural network section 12 must convert each set of color analysis data values of each picture element of the original to obtain a corresponding set of printing density data values which, when supplied to a printer, will result in a picture element being printed which is close to the original picture element with respect to hue, color density, and gray-scale value. The flow of sets of printing density data values that are obtained by such conversion will be referred to in the following as the printing density signal B. The above remarks are equally applicable to a monochrome image copier system, but with only gray-scale correction being executed by the neural network section 12 in that case, to obtain the printing density signal B.
The printer unit 13 in this example is of the type whose operation is based on formation of a copy of an original image as a pattern of electrical charge on the surface of a a rotary drum having photo-conductive surface, i.e. with that charge image being formed by first charging the drum surface uniformly to a specific potential, in darkness, and then selectively discharging regions of the surface by scanning the surface with a light beam that is modulated in accordance with the color printing signal data, and with the charge image then being used to transfer to an output medium (i.e. paper sheet) an image formed of toner (pigment powder particles), and the pigment image then being "fixed", e.g. by application of heat.
In a practical apparatus, due to the respectively different speeds of operation of the scanning and analysis section 11, the neural network section 12 and the printer unit 13, the scanning data signals A and the printing density data signals B may be produced by readout from respective memories into which data generated by the scanning and analysis section 11 and the neural network section 12 respectively have been temporarily stored, and it will be assumed that such a memory is incorporated into the scanning and analysis section 11 and also into the neural network section 12. In addition, the neural network section 12 includes a parameter memory, for storing internal parameters (i.e. weighting values) of the neural network. The operation of such an apparatus is as follows. Firstly, prior to executing a "learning" operation to derive and store a set of internal parameters of the neural network section 12, with the neural network section 12 disconnected from the printer unit 13, a sample image, such as a large number of color samples (sometimes called color patches) covering a wide range of hue and gray-scale value, is printed by applying successive predetermined tricolor printing density data values to the printer unit 13. The respective sample printing density data value sets for these color patches are stored in memory. Next, the scanning and analysis section 11 is set up to successively scan the color patches, scanning is executed of all of the color patches, and the resultant values of tricolor scanning data signals A are stored in memory. Within the neural network section 12, comparators are provided for comparing the printing density data signals B produced from the neural network section 12 with respective ones of the tricolor sample data value sets that have been stored as described above. The stored values of the tricolor scanning data signals A for the first color patch are then read out from memory, to be compared with the tricolor printing density data values that were actually used to print that color patch (i.e. are compared with a set of ideal printing density values as reference values) which are read out from memory at the same time, and computations are then executed (based on an amount of error that is obtained as a result of that comparison) for correcting the internal parameters of the neural network section 12. That process is then repeated for the next color patch, and so on. When that comparison and parameter correction process has been executed for all of the color patches, it is thereafter cyclically repeated until it is determined that satisfactory values for the internal parameters of the neural network section 12 have been reached, i.e. that a sufficient degree of convergence for the values of the internal parameters of the neural network has been reached, so that the output values produced from the neural network in response to specific input values supplied thereto are sufficiently close to the corresponding reference values indicative of predetermined tricolor printing density data values.
Algorithms for executing successive correction of the internal parameters of a neural network, based on a comparison between output values from the neural network and reference data values, are well known, and no detailed discussion will be given herein.
In a practical apparatus, the neural network of the section 12 can be configured by simulating the operation of a neural network by a suitably programed microprocessor, used in conjunction with a RAM (random access memory) for implementing the various memory functions described above.
After the learning operation described above has been completed, the neural network section 12 will provide a non-linear relationship between tricolor scanning data signals A that are produced from the scanning and analysis section 11 when any arbitrary original color image 10 is scanned and the corresponding printing density data signals B that are provided by the neural network section 12, such as to result in printing operation by the printer unit 13 that produces accurate reproduction for the output copy 14, even for tone values in the original image that are intermediate between tone values of the color patches.
However with such an image copier apparatus, the relationship between the printing signal data that are supplied to the printing apparatus for a picture element and the tonal values (i.e. hue, color saturation and gray-scale values in the case of color reproduction) of a picture element that is actually printed in response to the printing signal data will vary in accordance with certain internal environmental conditions of the printer unit 13. These include the temperature and humidity levels within the printer apparatus, the potential to which the drum surface is pre-charged, the degree of moisture contained in the paper that is used as the copying output medium, etc. Certain operating conditions (e.g. a drive voltage that is applied to a light-emitting element which produces a light beam that is modulated to scan the rotating drum, and the level of voltage to which the drum surface is pre-charged before scanning by that light beam) of the printer, these being referred to in the following as the settable operating values of the printer, can be manually adjusted such as to provide improved reproduction quality. It will thereafter not be necessary to change these settable operating values so long as the internal environmental conditions of the printer are not changed. However it will be necessary for the operator of such an apparatus to manually execute adjustment of these settable operating values each time that the apparatus is used in a different operating environment (e.g. is moved to a warmer room, for example). This is a disadvantage of such an apparatus.
Moreover, with the apparatus example of FIG. 1, the values of neural network internal parameters that are derived by the aforementioned "learning" operation using a large number of color samples may only provide accurate reproduction when the apparatus is used with internal environmental conditions that are close to those under which the "learning" operation was executed. That is to say, if the apparatus is later transferred for example to a warmer or colder room, then it may be necessary to repeat the neural network internal parameter learning process in order to obtain a new set of internal parameters which will provide accurate reproduction, i.e. in order to counteract the effects of the change in internal environmental conditions of the printer upon the printing process. Thus, whenever there is a substantial change in the internal environmental conditions of the printer, it may be necessary to not only adjust the settable, operating values of the printer, but also to repeat a neural network learning operation.
Thus, it is difficult to achieve consistently high quality of reproduction, even in the case of an apparatus of the type described above in which a neural network is used to derive the printing density data signals B from the tricolor scanning data signals A.